Magnetic resonance imaging (MRI) of the head and brain is a powerful tool for research and diagnosis. During a MRI scan patients are asked to keep their head very still because slight movements can spoil the MRI data, but this can be difficult for young children, elderly people, and those who suffer from Parkinson's disease, schizophrenia, epilepsy, and dementia. Our research will let MRI better serve these patients by allowing accurate data to be collected even when head movements occur during scanning. The standard approach to correct motion artifacts in MRI is retrospective image-based motion detection and correction as implemented in popular analysis packages such as SPM and AIR. This approach is well suited to motion within the imaging plane, but cannot handle substantial through-plane motion which both cannot be described by a single rotation/translation and alters the spin magnetization history of the tissue in the imaging field of view (FOV). Prospective motion correction techniques which measure head position in real time and adjust the FOV prior to data acquisition thus offer a compelling advantage for through-plane motion. However, existing 'prospective techniques such as navigator echoes and PACE impose a delay in data acquisition rates. Our objective is to implement and validate a novel scheme for prospective correction of MRI motion artifact that operates in parallel with the acquisition of imaging data, preventing temporal delay. We have developed a tracking device for real-time monitoring of three- dimensional changes in head position using three RF tracking coils for spatial localization simultaneous with image data acquisition via a standard head coil. Our first specific aim is to implement dynamic motion detection using our tracking device and prospective re-alignment of the imaging plane on a Philips Achieva scanner. Our second specific aim is to create realistic motion artifacts in MRI data from phantoms. Four metrics will be used to evaluate the success of the correction algorithm. Our third specific will study twelve volunteers who have been instructed to turn their heads to track a moving visual stimulus. The metrics used to evaluate the algorithm consist of a) comparison with standard retrospective motion correction using AIR, b) evaluation of the high spatial frequencies present in the images collected with and without the motion correction scheme, c) comparison of line profiles through the images of corrected vs. uncorrected images and d) measurement of the width at = height of small cylinders in one of the phantoms. We expect that our scheme will be better able to address the degree of motion typically seen in patient populations.